Mid-gestation fetal cortex dataset: Cluster characterization¶

Single cell transcriptomics dataset from paper published by Trevino et al. (Cell 2021) characterizing human fetal cortex at mid-gestation.

Upstream Steps

  • Assemble adata
  • QC filter on cells
  • Expression filter on genes
  • Normalization and log10 transformation by Scanpy functions
  • Feature selection (HVG) by Scanpy functions
  • Dimensionality reduction
  • Batch correction by Harmony
  • Cluster identification

This notebook

  • Cluster characterization

1. Environment Set Up¶

1.1 Library upload¶

In [1]:
import os
import sys

import numpy as np
import pandas as pd
import igraph as ig
import scanpy as sc
import scanpy.external as sce
from scipy.sparse import csr_matrix, isspmatrix
import gseapy as gp

#Plotting
import matplotlib.pyplot as plt
import seaborn as sns

#ultils
#import ipynbname
from datetime import datetime
In [2]:
# Custom functions
sys.path.append('HelperFunctions')
import Day1Helper as fn
In [3]:
sc.settings.verbosity = 3
sc.settings.set_figure_params(dpi=80, fontsize=12)

1.2 Start Computation time¶

In [4]:
print(datetime.now())

2024-11-10 10:56:32.410390

2. Read input files¶

In [5]:
path = '../DataDir/InputData/'
Id = 'Day1_2_TrevinoFiltNormAdata.h5ad'

input_file = path + Id

adata = sc.read(input_file)
In [6]:
print('Loaded Normalizes AnnData object: number of cells', adata.n_obs)
print('Loaded Normalizes AnnData object: number of genes', adata.n_vars)

print('Available metadata for each cell: ', adata.obs.columns)

Loaded Normalizes AnnData object: number of cells 45888
Loaded Normalizes AnnData object: number of genes 16324
Available metadata for each cell:  Index(['Auth_Sample.ID', 'Auth_Age', 'Auth_Tissue.ID', 'Auth_Sample.Type',
       'Auth_Assay', 'Auth_Batch', 'Auth_seurat_clusters', 'Auth_RNA.Counts',
       'Auth_RNA.Features', 'Auth_Percent.MT', 'Auth_Percent.Ribo',
       'Auth_Cell.Barcode', 'Auth_DF_pANN', 'Auth_DF_classification',
       'Auth_DF_pANN_quantile', 'Auth_Spliced.Counts', 'Auth_Spliced.Features',
       'Auth_Unspliced.Counts', 'Auth_Unspliced.Features',
       'Auth_Ambiguous.Counts', 'Auth_Ambiguous.Features', 'dataset_id',
       'sample_id', 'brain_region', 'age', 'stage', 'batch_key', 'cell_label',
       'cell_class', 'n_genes_by_counts', 'log1p_n_genes_by_counts',
       'total_counts', 'log1p_total_counts', 'total_counts_mito',
       'log1p_total_counts_mito', 'pct_counts_mito', 'total_counts_ribo',
       'log1p_total_counts_ribo', 'pct_counts_ribo', 'gene_UMI_ratio',
       'log1p_gene_UMI_ratio', 'n_genes', 'n_counts', 'Leiden_02', 'Leiden_04',
       'Leiden_06', 'Leiden_10', 'Leiden_Sel'],
      dtype='object')

3. Cluster characteristics¶

3.1 Check with original annotation¶

In [7]:
sc.settings.set_figure_params(fontsize=9, figsize=[8, 6])

sc.pl.embedding(adata, basis="X_umap_harmony", color=['Leiden_Sel','cell_label', 'cell_class'],
                legend_loc='on data')
No description has been provided for this image
In [8]:
pd.crosstab(adata.obs['cell_class'], adata.obs['Leiden_Sel'], normalize = 'columns', margins=True)
Out[8]:
Leiden_Sel 0 1 2 3 4 5 6 7 8 9 10 11 12 All
cell_class
Pg 0.647059 0.914141 0.000904 0.124337 0.001495 0.0 0.000231 0.988905 0.000574 0.003520 0.0 0.005505 0.009967 0.168214
Other 0.000000 0.000459 0.000452 0.000530 0.001121 1.0 0.000692 0.000236 0.000287 0.000880 0.0 0.000000 0.039867 0.006450
ExN 0.000000 0.000000 0.995633 0.615589 0.992713 0.0 0.000462 0.000708 0.001147 0.978443 0.0 0.110092 0.946844 0.598196
IPC 0.001898 0.061983 0.000753 0.256098 0.000374 0.0 0.000923 0.009443 0.000287 0.000000 0.0 0.009174 0.000000 0.025388
InN 0.000000 0.000000 0.000301 0.001326 0.001246 0.0 0.994923 0.000000 0.995985 0.016278 0.0 0.875229 0.003322 0.181442
Microglia 0.000000 0.000459 0.001657 0.001856 0.002803 0.0 0.002539 0.000472 0.001721 0.000880 1.0 0.000000 0.000000 0.006930
OPC_Oligo 0.351044 0.022957 0.000301 0.000265 0.000249 0.0 0.000231 0.000236 0.000000 0.000000 0.0 0.000000 0.000000 0.013380

_Try yourself_: what if you want to see total number of cells instead of %?

In [9]:
# write your code here

_Try yourself_: what if you want to compare with the more granular annotation?

In [10]:
# write your code here

Comparison outcome¶

From the comparison above, we can hypothesize a first large-grain definition of our clusters.

  • Excitatory Neurons: C2, C3 (IPC), C4, C9
  • Inhibitory Neurons: C6, C8, C11
  • Progenitors: C1, C7
  • Glia: C0 (progenitors, OPC), C10 (microglia)
  • Other: C5

3.2 Cluster dendrogram¶

In [11]:
sc.settings.set_figure_params(fontsize=10, figsize=[8, 6])

sc.tl.dendrogram(adata, groupby='Leiden_Sel', n_pcs=5, cor_method='spearman', linkage_method='average')
sc.pl.dendrogram(adata, groupby='Leiden_Sel', orientation='left')

    using 'X_pca' with n_pcs = 5
Storing dendrogram info using `.uns['dendrogram_Leiden_Sel']`
No description has been provided for this image
Out[11]:
<Axes: >

_Try yourself_: How to compare to a dendrogram created on the annotation from the original paper?

In [12]:
# write your code here

4. Population markers¶

  • Supervised approach: plot on the UMAP your own set of population markers
  • The definition of the markers strongly depends on biological knowledge and liteature survey, and it is context-dependent
  • As an example, we define below a dictionary of markers usually checked in cortex datasets (not comprehensive!!)
  • We use the customUmap function defined in the helper file: check there to go back to the basic scanpy function, for the fine-tuning of the parameters or to create your own custom function
In [13]:
marker_dictionary = {

    'Proliferating_Progenitors': ['MKI67', 'CDC20', 'HMGB2', 'CCNB1', 'CCNB2', 'ASPM'], 
    'Radial_Glia': ['SOX2', 'PAX6', 'NES', 'VIM', 'HES1', 'GLI3'],
    'Intermediate_Progenitors': ['EOMES', 'ELAVL4', 'NHLH1', 'KCNQ3', 'INSM1', 'HES6'], 
    'oRG': ['FAM107A', 'HOPX',  'PTPRZ1', 'TNC', 'ITGB5'], 
    
    'Neurons': ['GAP43', 'DCX', 'STMN2', 'MAP2', 'SYT1', 'MEF2C'], 
    
    'Excitatory_Progenitors': ['EMX1', 'NEUROD1', 'NEUROD2', 'NEUROD6', 'NEUROG2', 'NEUROG1'],
    'Excitatory_Neurons': ['SLC17A6', 'SLC17A7', 'GRIN2A', 'GRIN2B', 'SLA'], 
    
    'Inhibitory_Early': ['NKX2-1', 'DLX5', 'DLX6', 'DLX1', 'DLX2', 'DLX6-AS1'],
    'Inhibitory_Neurons': ['GAD1', 'GAD2', 'SLC32A1', 'CALB1', 'CALB2', 'NPY', 'SST', 'PVALB', 'VIP'],
    
    'Astrocytes': ['GFAP', 'SLC1A3',  'S100B', 'AQP4', 'ALDH1L1', 'TNC'], 
    'Microglia': ['PTPRC', 'AIF1', 'CCL3', 'ITGAM', 'CX3CR1', 'CD74'], 
    
    'Endothelial_Pericytes': ['CLDN5', 'PECAM1', 'ABCG2', 'FOXC2', 'PDGFRB', 'IFITM2'], 
    }
In [14]:
sc.settings.set_figure_params(dpi=80, fontsize=12)

for population in marker_dictionary: 
    print(f"{population.upper()}:")
    fn.customUmap(adata, marker_dictionary[population], size=8)
    print("\n\n\n")

PROLIFERATING_PROGENITORS:

The following marker genes are missing:  set()
No description has been provided for this image 



RADIAL_GLIA:

The following marker genes are missing:  set()
No description has been provided for this image 



INTERMEDIATE_PROGENITORS:

The following marker genes are missing:  set()
No description has been provided for this image 



ORG:

The following marker genes are missing:  set()
No description has been provided for this image 



NEURONS:

The following marker genes are missing:  set()
No description has been provided for this image 



EXCITATORY_PROGENITORS:

The following marker genes are missing:  set()
No description has been provided for this image 



EXCITATORY_NEURONS:

The following marker genes are missing:  set()
No description has been provided for this image 



INHIBITORY_EARLY:

The following marker genes are missing:  set()
No description has been provided for this image 



INHIBITORY_NEURONS:

The following marker genes are missing:  {'PVALB'}
No description has been provided for this image 



ASTROCYTES:

The following marker genes are missing:  set()
No description has been provided for this image 



MICROGLIA:

The following marker genes are missing:  {'ITGAM'}
No description has been provided for this image 



ENDOTHELIAL_PERICYTES:

The following marker genes are missing:  set()
No description has been provided for this image

_Try yourself_: define below a small dictionary of genes you are interested in and plot them on the UMAP

In [15]:
# write your code here

5. Cluster Top Markers¶

5.1 Identify Cluster top-markers¶

For each cluster, top-marker genes are identified by comparing the expression in that cluster versus all the others. Scanpy tl.rank_genes_groups function is used to this purpose.

  • groupby: cluster labels
  • method: statistical method for differential expression analysis. Here we specify Wilcoxon test (non-parametric).
In [16]:
adata.uns['log1p']['base'] = None
sc.tl.rank_genes_groups(adata, groupby='Leiden_Sel', method='wilcoxon', key_added='wilcox', 
                       use_raw=False, pts=True)

ranking genes
    finished: added to `.uns['wilcox']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:01:02)
In [17]:
GroupMarkers = pd.DataFrame(adata.uns['wilcox']['names']).head(101)
GroupMarkers.columns = 'Cl_' + GroupMarkers.columns

GroupMarkers.head(11)
Out[17]:
Cl_0 Cl_1 Cl_2 Cl_3 Cl_4 Cl_5 Cl_6 Cl_7 Cl_8 Cl_9 Cl_10 Cl_11 Cl_12
0 BCAN HMGB2 SATB2 HES6 ENC1 IGFBP7 DLX6-AS1 VIM ERBB4 MEF2C FTL DLX6-AS1 TMSB10
1 DBI TOP2A STMN2 TMEM158 GRIA2 IFITM3 BTG1 TTYH1 MAF NELL2 SPP1 ERBB4 RPL10
2 OLIG1 HMGB1 LIMCH1 EOMES POU3F2 GNG11 CCDC88A PTN DLX6-AS1 GRIN2B AIF1 DLX5 STMN2
3 PTPRZ1 TUBA1B GPM6A PRDX1 MLLT3 SPARC DLX5 FABP5 PDE4DIP ARPP21 TYROBP DCX STMN1
4 OLIG2 HMGN2 SLA PPP1R17 MEIS2 FN1 DLX1 SLC1A3 ARX CALM1 B2M DLX2 TUBB2B
5 CSPG5 H2AZ1 NEUROD2 SSTR2 CPE B2M ERBB4 ID4 DLX2 MAP1B RGS10 ARX RPL13
6 METRN CENPF SYT4 CCND2 NFIB MYL12A DLX2 CLU DCX CHL1 ITM2B PDE4DIP RPL26
7 PDGFRA NUSAP1 ADRA2A MIR99AHG NEUROD2 ARHGAP29 TCF4 GPM6B CXCR4 CELF2 S100A11 DLX1 TUBA1A
8 QKI MKI67 BCL11A NFIA ROBO2 COL4A1 NR2F2 SFRP1 C11orf96 GAP43 SAT1 PLS3 TUBB
9 GPM6B SMC4 TUBA1A EEF1D NTM COL4A2 MEG3 ZFP36L1 PLS3 SLITRK5 CTSB CCDC88A PTMA
10 CST3 H2AX DAB1 NHLH1 SORBS2 JUNB PCDH9 PEA15 RBP1 TUBB2A TPT1 SLAIN1 RPS18

5.2 Visualize marker genes¶

DotPlot¶

In [18]:
sc.pl.rank_genes_groups_dotplot(adata, n_genes=4, key='wilcox', standard_scale="var")  
# standard scale: normalize each gene to range from 0 to 1
No description has been provided for this image

5.3 Filter top marker genes¶

  • To improve the specificity of the markers, we can filter top-markers genes, e.g. considering ther % of expression in the target population and in the other cells.
  • We can then plot again the results with dotplot or matrixplot
In [19]:
sc.tl.filter_rank_genes_groups(
    adata,
    min_in_group_fraction=0.3,
    max_out_group_fraction=0.2,
    key="wilcox", key_added="wilcox_filt",
)

Filtering genes using: min_in_group_fraction: 0.3 min_fold_change: 1, max_out_group_fraction: 0.2
In [20]:
sc.pl.rank_genes_groups_dotplot(adata, n_genes=4, key='wilcox', 
                               standard_scale="var")
No description has been provided for this image
In [21]:
sc.pl.rank_genes_groups_dotplot(
    adata, key='wilcox', n_genes=4,
    values_to_plot="logfoldchanges",
    cmap='bwr', vmin=-4, vmax=4, min_logfoldchange=2,
    colorbar_title='log fold change',
)
No description has been provided for this image
In [22]:
sc.pl.rank_genes_groups_matrixplot(adata, n_genes=5, key='wilcox_filt', standard_scale="var")

WARNING: No genes found for group 12
WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different.
categories: 0, 1, 2, etc.
var_group_labels: 0, 1, 2, etc.
No description has been provided for this image

5.4 Marker overlap¶

  • To gain further information on our clusters, we can overlap the top-genes of each cluster with external gene sets (e.g. reliable sets of population marker genes; genes identified by other studies, etc.).
  • As an example, here we overlap the marker dictionary defined above
In [23]:
marker_overlap = sc.tl.marker_gene_overlap(adata, marker_dictionary, key='wilcox', 
                         top_n_markers=100, method='jaccard')

marker_overlap.columns = marker_overlap.columns.astype('category')
In [24]:
sns.set_theme()
f, ax = plt.subplots(figsize=(10, 7))
sns.heatmap(marker_overlap, linewidths=.5, ax=ax, cmap = 'PuBu')
Out[24]:
<Axes: >
No description has been provided for this image

6. Functional analysis by gseapy¶

6.1 Approaches¶

Overview¶

Two main approaches can be employed

  • over-representation analysis: test for enrichment of functionally relevant gene sets in the pool of the gene of interest (e.g. genes selected as cluster markers, DEGs) compared to the gene universe (all expressed/tested genes).
  • gsea-like approaches: rank all tested genes according to statistical metrics (e.g. PValue, Fold-Change, etc.) and test the distribution of the gene sets along the list (e.g. more present in the top-part of the list vs evenly distributed).

Each of these methods has advantages and drawbacks. In general, take into consideration that functional enrichment analyses are just a way to try to describe from a functional point of view your list of genes; they rely on vocabularies that can be variably complete and tuned to your biological system. Considering this, it is wiser to use the results as complementary to evidence coming from other approaches, rather than blindly and acritically rely on them.

Gseapy¶

  • Read the docs
  • Bioinformatics Paper
  • GitHub repo

Other tools¶

  • GProfiler
  • Decoupler
  • TopGO
In [25]:
# to check available gene sets
gp.get_library_name()[:5]
Out[25]:
['ARCHS4_Cell-lines',
 'ARCHS4_IDG_Coexp',
 'ARCHS4_Kinases_Coexp',
 'ARCHS4_TFs_Coexp',
 'ARCHS4_Tissues']

6.2 Pre-ranked GSEA¶

  • the custom function customGseapy (see helper file) first generates the list of marker genes ranked according to scanpy-calculated score for the cluster of interest
  • GSEA is then applied to the pre-ranked list for the gene sets of interests
  • as an example, here we test two genes sets (GO Biological Process; Cell Markers collection) on a subset of clusters.
In [26]:
# C1: progenitors - C8: inhibitory neurons - C9: excitatory neurons - C10: microglia 

Cl = ['1', '8' , '9', '10']

for i in Cl: 
    print("\n\n {}".format(i)) 
    display(fn.customGseapy(adata, cluster=i, rank='wilcox', 
                            sets = ['GO_Biological_Process_2023'],
                            fdr_th=0.005, nes_th=1.75, show=5))

2024-11-10 10:58:12,245 [WARNING] Duplicated values found in preranked stats: 0.10% of genes
The order of those genes will be arbitrary, which may produce unexpected results.


 1
nes pval fdr fwerp tag % gene % lead_genes
GO_Biological_Process_2023__Positive Regulation Of Cell Cycle Process (GO:0090068) 1.908707 0.0 0.0 0.0 53/95 7.06% NUSAP1;SMC4;MAD2L1;BIRC5;SMC2;NDC80;NCAPG;ECT2...
GO_Biological_Process_2023__Mitotic Spindle Organization (GO:0007052) 1.876042 0.0 0.0 0.0 40/82 6.28% TPX2;BIRC5;NUF2;KIF11;NDC80;CENPE;RAN;SPC25;DL...
GO_Biological_Process_2023__Mitotic Sister Chromatid Segregation (GO:0000070) 1.865853 0.0 0.0 0.0 54/107 6.73% NUSAP1;SMC4;TPX2;SMC2;KIF11;ZWINT;NDC80;NCAPG;...
GO_Biological_Process_2023__Mitotic Spindle Assembly (GO:0090307) 1.859971 0.0 0.0 0.0 22/42 6.28% TPX2;BIRC5;KIF11;RACGAP1;AURKB;KIF23;KIF4A;PRC...
GO_Biological_Process_2023__Chromosome Condensation (GO:0030261) 1.857803 0.0 0.0 0.0 18/27 3.91% TOP2A;NUSAP1;SMC4;SMC2;NCAPG;H1-3;H1-2;H1-5;H1... 
2024-11-10 10:58:26,147 [WARNING] Duplicated values found in preranked stats: 0.52% of genes
The order of those genes will be arbitrary, which may produce unexpected results.


 8
nes pval fdr fwerp tag % gene % lead_genes 
2024-11-10 10:58:38,709 [WARNING] Duplicated values found in preranked stats: 0.18% of genes
The order of those genes will be arbitrary, which may produce unexpected results.


 9
nes pval fdr fwerp tag % gene % lead_genes
GO_Biological_Process_2023__Negative Regulation Of Stress Fiber Assembly (GO:0051497) 1.808246 0.0 0.004138 0.005 6/20 3.48% STMN1;PIK3R1;TMEFF2;CLASP1;DYNLL1;CLASP2
GO_Biological_Process_2023__Synaptic Vesicle Recycling (GO:0036465) 1.791272 0.0 0.004138 0.01 27/42 16.86% DNM3;SNCA;VAMP2;RAB3A;AP3S1;SYT5;SYT1;AP2B1;DN...
GO_Biological_Process_2023__Regulation Of Monoatomic Ion Transmembrane Transporter Activity (GO:0032412) 1.783807 0.0 0.002758 0.01 28/59 15.87% MEF2C;SCN3B;YWHAH;FGF12;ANK2;CACNG8;SHISA9;MAP...
GO_Biological_Process_2023__Negative Regulation Of Actin Filament Bundle Assembly (GO:0032232) 1.775092 0.0 0.003103 0.015 6/23 3.48% STMN1;PIK3R1;TMEFF2;CLASP1;DYNLL1;CLASP2
GO_Biological_Process_2023__Neurotransmitter Secretion (GO:0007269) 1.76505 0.0 0.00331 0.02 26/39 15.94% PCLO;SNCA;VAMP2;RAB3A;NRXN2;SYT1;STXBP1;STX1A;... 
2024-11-10 10:58:50,167 [WARNING] Duplicated values found in preranked stats: 7.74% of genes
The order of those genes will be arbitrary, which may produce unexpected results.


 10
nes pval fdr fwerp tag % gene % lead_genes
GO_Biological_Process_2023__Cytokine-Mediated Signaling Pathway (GO:0019221) 2.235324 0.0 0.0 0.0 55/135 10.23% CCL3;TMSB4X;FCER1G;CCL4;CSF1R;PYCARD;NFKBIA;CS...
GO_Biological_Process_2023__Positive Regulation Of Phagocytosis (GO:0050766) 2.222753 0.0 0.0 0.0 21/38 4.57% TREM2;CYBA;FCER1G;C3;PYCARD;PTPRC;MERTK;TNF;AP...
GO_Biological_Process_2023__Inflammatory Response (GO:0006954) 2.215576 0.0 0.0 0.0 50/119 10.65% AIF1;CYBA;CCL3;FOLR2;ITGB2;CCL4;CSF1R;OLR1;CEB...
GO_Biological_Process_2023__Positive Regulation Of Cytokine Production (GO:0001819) 2.207105 0.0 0.0 0.0 75/195 8.17% AIF1;TYROBP;CYBA;LAPTM5;FCER1G;HLA-E;C3;CSF1R;...
GO_Biological_Process_2023__Cellular Response To Cytokine Stimulus (GO:0071345) 2.19582 0.0 0.0 0.0 79/193 10.65% AIF1;CCL3;ASAH1;CCL4;CSF1R;PYCARD;IFI16;ZFP36L...

7. Examine a gene list of interest¶

7.1 Define gene signatures¶

In [27]:
semaforins = fn.selectMarkers(adata, ["SEMA3A","SEMA3B","SEMA3C","SEMA3E","SEMA3F","SEMA4A","SEMA4B",
              "SEMA4C","SEMA4D","SEMA4F","SEMA4G","SEMA5A","SEMA5B","SEMA6A","SEMA6B","SEMA6C","SEMA6D","SEMA7A"])

#Kegg Steroid biosynthesis genes
steroid = fn.selectMarkers(adata, ["CEL","CYP27B1","CYP51A1","DHCR24","DHCR7","EBP","FDFT1","HSD17B7","LIPA","LSS","MSMO1",
                                   "NSDHL","SC5D","SOAT1","SOAT2","SQLE","TM7SF2"])

TFs = fn.selectMarkers(adata, ['DLX5', 'DLX1', 'SOX9', 'MEIS2', 
      'NEUROG2', 'NEUROD1', 'LHX5', 'LHX2', 
      'LHX9', 'EN1', 'EMX2', 'EMX1'])

The following marker genes are missing:  set()

The following marker genes are missing:  {'CYP27B1', 'SOAT2'}

The following marker genes are missing:  {'EN1'}

7.2 Visualize levels across clusters¶

Plot 'semaforins' across clusters by Stacked Violins¶

In [28]:
sc.settings.set_figure_params(figsize=[10, 8])
sc.pl.StackedViolin(adata, semaforins, groupby='Leiden_Sel').show()
No description has been provided for this image

Plot 'TFs' across clusters and original annotation by dotplots anda matrixplot¶

In [29]:
sc.pl.dotplot(adata, TFs, groupby='Leiden_Sel', swap_axes=True)
No description has been provided for this image
In [30]:
sc.pl.dotplot(adata, TFs, groupby='cell_label', standard_scale="var", cmap='Blues', swap_axes=True)
No description has been provided for this image
In [31]:
sc.pl.matrixplot(adata, TFs, groupby='Leiden_Sel', swap_axes=True, standard_scale="var")
No description has been provided for this image

_Try yourself_: plot genes involved in steroid synthesis stratified by clusters or cell labels

In [32]:
# write your code here

7.3 Calculate and visualize gene scores¶

For a gene signature that you expect to be expressed coherently in the same cell population, you can employ the tl.score.genes to estimate an average expression score, and then plot it on the UMAP.

In [33]:
sc.tl.score_genes(adata, steroid, score_name='steroid_score')

computing score 'steroid_score'
    finished: added
    'steroid_score', score of gene set (adata.obs).
    549 total control genes are used. (0:00:00)
In [34]:
sc.pl.embedding(adata, basis="X_umap_harmony", 
                color='steroid_score')
No description has been provided for this image

8. Save¶

8.1 Timestamp finished computations¶

In [35]:
print(datetime.now())

2024-11-10 10:59:09.538057

8.2 Save python and html versions¶

In [36]:
nb_fname = '2_Clusters'
nb_fname
Out[36]:
'2_Clusters'
In [37]:
%%bash -s "$nb_fname"
jupyter nbconvert "$1".ipynb --to="python"
jupyter nbconvert "$1".ipynb --to="html"

[NbConvertApp] Converting notebook 2_Clusters.ipynb to python
[NbConvertApp] Writing 12310 bytes to 2_Clusters.py
[NbConvertApp] Converting notebook 2_Clusters.ipynb to html
[NbConvertApp] WARNING | Alternative text is missing on 14 image(s).
[NbConvertApp] Writing 15199570 bytes to 2_Clusters.html
In [ ]: